Statistical Analysis and Modeling of Brain Tumor Data: Histology and Regional Effects
Comprehensive statistical models for non-normally distributed cancerous tumor sizes are of prime importance in epidemiological studies, whereas a long term forecasting models can facilitate in reducing complications and uncertainties of medical progress. The statistical forecasting models are critic...
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| Format: | Others |
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Scholar Commons
2013
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| Online Access: | http://scholarcommons.usf.edu/etd/4746 http://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=5943&context=etd |
| Summary: | Comprehensive statistical models for non-normally distributed cancerous tumor sizes are
of prime importance in epidemiological studies, whereas a long term forecasting models
can facilitate in reducing complications and uncertainties of medical progress. The statistical
forecasting models are critical for a better understanding of the disease and supply
appropriate treatments. In addition such a model can be used for the allocations of budgets,
planning, control and evaluations of ongoing efforts of prevention and early detection of
the diseases.
In the present study, we investigate the effects of age, demography, and race on primary
brain tumor sizes using quantile regression methods to obtain a better understanding of the
malignant brain tumor sizes. The study reveals that the effects of risk factors together with
the probability distributions of the malignant brain tumor sizes, and plays significant role in
understanding the rate of change of tumor sizes. The data that our analysis and modeling is
based on was obtained from Surveillance Epidemiology and End Results (SEER) program
of the United States.
We also analyze the discretely observed brain cancer mortality rates using functional
data analysis models, a novel approach in modeling time series data, to obtain more accurate
and relevant forecast of the mortality rates for the US. We relate the cancer registries,
race, age, and gender to age-adjusted brain cancer mortality rates and compare the variations
of these rates during the period of the study that data was collected.
Finally, in the present study we have developed effective statistical model for heterogenous
and high dimensional data that forecast the hazard rates with high degree of accuracy,
that will be very helpful to address subject health problems at present and in the future. |
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